Attention mechanisms are also widely used in FGVC tasks, such as MACN[1]}, WS-DAN[2]} and CAL[3]} to learn the location of objects, and extract the characteristics of a specific location in a feature map through the attention-map. In addition to being used to learn location information, the attention mechanism can also improve the expressiveness of features. For example, MAMC[4]} and API-Net[5]} learn unique features by simultaneously training the differences between a pair of similar images through the attention mechanism, and CAP[6]} fuses different local features through the attention mechanism. The above methods all show that the attention mechanism is a very powerful method.
